8,669 research outputs found
Deep Learning Data and Indexes in a Database
A database is used to store and retrieve data, which is a critical component for any software application. Databases requires configuration for efficiency, however, there are tens of configuration parameters. It is a challenging task to manually configure a database. Furthermore, a database must be reconfigured on a regular basis to keep up with newer data and workload. The goal of this thesis is to use the query workload history to autonomously configure the database and improve its performance. We achieve proposed work in four stages: (i) we develop an index recommender using deep reinforcement learning for a standalone database. We evaluated the effectiveness of our algorithm by comparing with several state-of-the-art approaches, (ii) we build a real-time index recommender that can, in real-time, dynamically create and remove indexes for better performance in response to sudden changes in the query workload, (iii) we develop a database advisor. Our advisor framework will be able to learn latent patterns from a workload. It is able to enhance a query, recommend interesting queries, and summarize a workload, (iv) we developed LinkSocial, a fast, scalable, and accurate framework to gain deeper insights from heterogeneous data
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Fine tuning distributed systems is considered to be a craftsmanship, relying
on intuition and experience. This becomes even more challenging when the
systems need to react in near real time, as streaming engines have to do to
maintain pre-agreed service quality metrics. In this article, we present an
automated approach that builds on a combination of supervised and reinforcement
learning methods to recommend the most appropriate lever configurations based
on previous load. With this, streaming engines can be automatically tuned
without requiring a human to determine the right way and proper time to deploy
them. This opens the door to new configurations that are not being applied
today since the complexity of managing these systems has surpassed the
abilities of human experts. We show how reinforcement learning systems can find
substantially better configurations in less time than their human counterparts
and adapt to changing workloads
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